Nanocarbon materials including carbon nanotube (CNT) and graphene nanoplatelets (GNP) are ideal reinforcements of high-performance aluminum matrix composites, due to their ultra-high strength and modulus, excellent thermal and electrical conductivities. However, nanocarbons have poor wettability with aluminum matrix. Meanwhile, the reaction of nanocarbon/aluminum interface at high temperatures causes formation of brittle Al4C3 and thereby, damaging the nanocarbon reinforcements. Both of the abovementioned scenarios not only limit the strengthening effect of the reinforcements, but also deteriorate the composite toughness. Therefore, the regulations on interface reaction and bonding, and the synergistic improvements in the strength and toughness are key problems for the research of nanocarbon/aluminum composites. Based on the bonding mechanisms of nanocarbon/aluminum interface, the regulating study and effects of nanocarbon/aluminum interface were reviewed from the perspectives of surface modification of nanocarbon and optimization of composite process. Subsequently, the studies on multi-dimensional network strengthening of CNT and GNP, and their hybrid strengthening with ceramic particles were summarized. It was proposed that the strength and toughness of the composites would be further improved by designing the reinforcing network consisted of CNT, GNP, and ceramic particles, according to their different structures and strengthening effects.
In the context of the “Dual carbon” strategy, green and low-carbon development has become a necessary route for the aerospace manufacturing industry development. Surface treatment process, as a key process of large aircraft parts processing, is characterized by multiple processing procedure, long processing flow, and multiple carbon emission sources, and there are problems of high energy consumption, low energy efficiency, and severe carbon emissions, which poses challenges to its green and low-carbon development. To this end, this study takes the surface treatment process of large aircraft parts as an object to define the carbon emission boundary of the surface treatment process, and analyze the carbon emission characteristics of this process. On this basis, a carbon emission accounting method for the surface treatment process of large aircraft parts based on the improved value stream mapping is proposed, and the carbon emission hotspot is obtained to elucidate the carbon reduction potential of the surface treatment process based on the sensitivity analysis. Finally, the boric-sulfuric acid anodic oxidation of the large aircraft aluminum alloy parts is performed to verify the effectiveness of the proposed method. This research can provide methodological support for the refined carbon emission modeling for surface treatment process, and also lays a theoretical foundation for the subsequent development of energysaving and carbon-reducing strategies.
The design of support layout for thin-walled composite components is an important method to suppress the vibration and deformation of their processing. However, most of the support layout optimization process only considers single vibration or deformation, and ignores influence of suction cup adsorption on the workpiece, which causes deviations from the actual working conditions. In this paper, a particle swarm optimization algorithm+finite element fusion-driven optimization method for support layout of the thin-walled components is proposed, which comprehensively considers adsorption deformation of the workpiece, effective separation of natural frequency of the workpiece and excitation frequency of the tool after supporting, and the additional auxiliary support, so as to optimize numbers and positions of the support points on the premise that the maximum deformation meets the requirements. Firstly, the support points were increased successively at the maximum deformation until the deformation requirements were met, then the support points were increased at the maximum amplitude of the vibration mode corresponding to the natural frequency that was easy to generate resonance, until the frequency requirements were met, thereafter the optimization algorithm was used to find the minimum number of support points and optimize the support layout on this basis. The results show that the proposed method can effectively reduce numbers of support points on the premise of ensuring the frequency and deformation requirements.
Intelligent machining of aerospace components has created an urgent demand for online monitoring and fault prediction, particularly in five-axis milling of thin-walled parts and complex curved surfaces. Accurate milling force prediction is critical for process optimization. As the commonly used crucial technique in components milling, five-axis milling is of the advantage of excellent adaptability, enabling its application in gas turbine blade, aero-engine blade and turbine components. However, the complex process conditions and cutting geometry in five-axis milling present significant challenges for precise identification of milling force coefficients. This paper proposes an online identification method for milling force coefficients that considers cutter orientation. By analyzing the milling force signals within a single cutter rotation cycle, precise online identification of milling force coefficients is achieved. Based on a mechanical model for five-axis milling force prediction, the influence of cutter inclination is systematically considered. Analytical expressions are developed for the characteristic lines, intersection lines, and projection lines defining the cutter–workpiece contact area boundaries. Additionally, an instantaneous undeformed chip thickness model incorporating cutter inclination is introduced, leading to the construction of a five-axis milling force coefficient identification model that accounts for cutter orientation effects. Five-axis milling experiments and online monitoring of cutting forces were conducted to compare the milling force coefficient identification and prediction accuracy with and without considering cutter orientation. Error analyses were performed for different models in five-axis milling force prediction. The experimental results show that the proposed method significantly improves the accuracy of milling force prediction. This study provides an essential methodological foundation and theoretical support for online monitoring, fault prediction, and process optimization in the intelligent machining of aerospace components.
In order to meet the requirements for high-precision and high-efficiency production of high-performance honeycomb sandwich structures, the research progress of precision forming technology of thin-walled titanium alloy honeycomb core and corrugated sheet are presented in this paper. Firstly, the components of thin-walled oneycomb sandwich structures and their excellent strength, stiffness and heat-resistant performance characteristics are introduced. Secondly, three main manufacturing methods for honeycomb cores are summarized, i.e., stretching, additive manufacturing and stamping/rolling. The advantages, disadvantages and applicable conditions of the three methods are analyzed. Further, the thin-walled corrugated sheets manufactured by forming method are analyzed in detail: how such thin-walled structures can be fabricated based on stamping or rolling process are expounded, the uniformity improvement of foil deformation, springback reduction of thin-walled structures and forming accuracy enhancement obtained by controlling the material orientation, process parameters and external energy field are summarized. Lastly, the challenges and development aspects of titanium alloy honeycomb sandwich structures and corrugated sheets in precision forming are prospected.
As an important part of structural dynamic analysis, modal parameters are the key to chatter prediction during milling of thin-walled components, and machine learning provides a new paradigm for traditional identification of structural modal parameters. However, for complex curved thin-walled parts, it is difficult to obtain the data in a specific environment and the amount of data collected would be large; uncertain factors such as high-dimensional nonlinear mapping relationships would affect the complex curved thin-walled parts as well. Therefore, a new method based on machine learning is proposed to identify the dynamic characteristics of curved thin-walled parts during milling process. Firstly, the state space model of curved thin-walled milling system is established, the continuous system is discretized, and the stochastic state space equation of generalized milling system discretized is derived. Secondly, based on the random subspace theory, modal parameters of the milling process of curved thin-walled parts are obtained. Then, the sliding window technology is used to reduce dimensionality of the data, extract the signal features, and establish the functional relationship between the input features and modal parameters through the neural network for modal parameter recognition, therefore, to realize the modal parameter recognition of curved thin-walled parts. Finally, milling dynamic parameters of the S-shaped standard part are obtained by using the method proposed in this study and analytical method, verifying accuracy of the proposed method.
6061 Aluminum alloy has low density, good corrosion resistance, oxidation resistance and weldability, therefore, is widely used in aerospace field. However, 6061 aluminum alloy has poor formability at room temperature, so hot forming technology is normally used to improve the quality of its thin-walled parts. The hot forming process of a double-C thin-walled part of 6061 aluminum alloy was studied in this paper; uniaxial hot tensile tests were conducted to investigate the deformation behavior of 6061 aluminum alloy under different temperatures and strain rates. By comprehensively considering the coupled effects of temperature and strain rate on forming quality, an improved Johnson–Cook (J–C) constitutive model was proposed to describe flow stress of materials, then parameters of the improved constitutive model were characterized using genetic algorithm. A finite element model of nonisothermal forming for the double-C thin-walled part was established, and orthogonal experiments and range analysis were conducted to rank the influence of various process parameters on stamping quality of the double-C thin-walled part. Latin hypercube sampling was employed to obtain training samples, and test samples were randomly generated. The maximum thinning rate of the double-C thin-walled part was taken as the optimization objective and simulation by ABAQUS was used to obtain response values for different samples, then an improved BP neural network was utilized to establish a mapping relationship between process parameters and the maximum thinning rate. The optimal combination of process parameters was obtained through an improved genetic algorithm, and the effectiveness of this method was verified through experiments.
Residual stress generated during the cutting process is one of the main factors causing deformation of thin-walled parts. The selection of cutting parameters has a direct impact on residual stress. By analyzing the variation law of residual stress and selecting cutting parameters reasonably, surface residual stress of the workpiece after cutting can be reduced, ultimately achieving the goal of controlling the degree of deformation of thin-walled parts and ensuring machining accuracy. Researches have been conducted on certain aspects, including change laws and influences of cutting force, cutting temperature and residual stress on the forming qualities of thin-walled parts. Based on previous studies, this paper proposed an improved algorithm to analyze the residual stress and optimize the process during formation of the thinwalled parts. Firstly based on McDowell model, this study established a theoretical model for three-dimensional residual stress, and analyzed the influence of spindle speed, feed rate per tooth and axial cutting depth on residual stress under highspeed cutting conditions through milling simulation; the results showed that the feed rate per tooth had the greatest impact on residual stress. Then based on the theoretical model and simulation analysis results, with the optimization objective of reducing residual stress and simultaneously increasing material removal rate, the Pareto simulated annealing algorithm was used for dual objective optimization of cutting parameters, and a set of optimal solutions was obtained. Finally, the optimization results were verified through milling experiments, and results showed that the error rate between residual stress values solved by the optimization algorithm and those measured by the experiment was within a reasonable range, indicating reliability of the optimization results and feasibility of the optimization algorithm.
Three-dimensional woven composites are a new-generation strategic materials that have been widely used in aerospace, national defense and other important fields due to their advantages of good overall structure performance, excellent interlayer performance and low preparation cost. The composites can be used as structural materials to bear load as well as functional materials to be applied in the abovementioned areas. Therefore, fabrication and corresponding mechanical property prediction of the composite are crucial for their future application. In this study, a new three-dimensional woven structure (multilayer–multiaxial interlock structure) was studied, tensile and in-plane shear tests of its composite material in two directions of 0° and 90° were carried out. By establishing a geometric single-cell model and selecting reasonable boundary conditions, stiffness prediction was carried out and compared with the experimental results. The results show that difference between the simulated value and experimental value of modulus of elasticity in the 0° direction is 1.73 GPa, difference in the 90° direction is 1.76 GPa, and the maximum error in both directions does not exceed 5%. The difference between the simulated value and experimental value of inplane shear modulus is 1.47 GPa and difference in Poisson’s ratio is 0.01, which is basically the same. The results indicate that modulus of elasticity predicted by the finite element simulation agrees well with the actual experimental values. This study provides references in terms of preparation of three-dimensional woven composites, data and experiment support for related studies.
Thermal error of spindle of the CNC machine tool is one of the main factors affecting machining accuracy of the machine tool. In order to improve accuracy of the thermal error measurement and reduce the measurement technology requirements, a thermal error information separation method of machine tool based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and empirical wavelet transform (EWT) is proposed. Firstly, the original signal is decomposed using the ICEEMDAN algorithm, the obtained low-frequency modal components are reconstructed and used as input of the EWT algorithm for decomposition, and the discrete coefficients are used to evaluate the decomposition effect of each iteration of the EWT algorithm. Secondly, accuracy of the ICEEMDAN–EWT algorithm was verified by decomposing a set of simulated signals, and root mean square error (RMSE) of the algorithm was reduced by 5.2% compared with the ICEEMDAN algorithm. Finally, experiments were conducted on a CKA6 163A machine tool to identify thermal errors using the five-point method, comparing the ICEEMDAN–EWT separation algorithm with the Fourier transform (FFT) algorithm. The experimental results show that compared with the FFT algorithm, the Pearson correlation of the five thermal deformation signals and machine tool temperature obtained by ICEEMDAN–EWT algorithm is improved by 3.8% and the Spearman correlation improved by 6.6%, indicating the proposed method is with higher accuracy.
The dry friction and wear tests of cemented carbide and Al–50% Si alloy were carried out to explore effects of load and sliding speed on the friction and wear properties of Al–50% Si alloy and wear mechanism of the cemented carbide. The results show that friction coefficient changes in three stages with time: running-in stage, transition stage and stable stage. The wear amount of Al–50% Si alloy increases with the increase of load and sliding speed. The wear mechanism of Al–50% Si alloy at low sliding load is delamination wear; when the load exceeds 50 N, the wear mechanism of Al–50% Si alloy changes to “abrasive wear+delamination wear”. At low sliding speed, the wear mechanism of Al–50% Si alloy is “delamination wear + abrasive wear”; with the increase of sliding speed, the wear mechanism is mainly delamination wear. Moreover, the surface of cemented carbide ball has wear features of scratch, adhesion, and delamination, and wear mechanism of adhesive wear and abrasive wear.
